搜索“Anthropic”

e gains continue, 4.7 will be interesting results 7:53 PM · Aug 12, 2026 · 5.21K Views 8 Replies · 1 Repost · 49 Likes SpaceX + Cursor is now the 3 frontier lab besides OpenAI and Anthropic (rip Gemini, unless their next model is actually good). Elon says Grok 4.7 will be ready in the next 3-4 weeks , and it’s currently being post-trained on SpaceX’s company data to make the model best at real-world engineering , if not everything. The model card for Grok 4.6 also covers many benchmarks beyond c

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pant in the world, a real player in the game—right now, at least among the people we can observe, it’s a galaxy of stars, an extremely vibrant field. Nobody can say Sam Altman, or Anthropic, is the ultimate winner, because every day our peers—especially, perhaps, the extremely smart, ambitious young people around Wudaokou, or in Hangzhou—keep bringing us surprises. This keeps monopoly from ever forming. That’s why you see so much firepower present at every layer of this industry. Zhang Xiaojun:

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anguage, 169 Chinese-language 🇨🇳) over 2026-08-01–2026-08-08. The Big Three Alibaba unveiled Qwen3.8-Max, a 2.4-trillion-parameter flagship it says posts benchmark scores rivaling Anthropic’s models. The release triggered an immediate rally in Alibaba’s Hong Kong shares and reads as the clearest marker yet that the “China is closing the gap” narrative has hardened into consensus. Alibaba made the model widely accessible ahead of an open-weights release for both the full model and a smaller Qwen3

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heated debate in the U.S. tech industry this year: Banning open models or not, particularly Chinese open-weight models. Silicon Valley has split into two camps. One is OpenAI and Anthropic, the world’s top two AI labs, which insist on a closed-source approach and reportedly lobby for policies that could constrain open models. The other is a coalition of tech companies led by Nvidia, Microsoft and Meta, which are strongly in favor of open-weight models. They signed a letter last week warning aga

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nd watched it top global coding leaderboards. The open release also became a Washington flashpoint: US lawmakers are now investigating DoorDash’s use of an earlier Kimi model, and Anthropic’s Dario Amodei weighed in on the broader open-weight debate, saying he isn’t opposed to open models in principle but worries about ceding ground to Chinese labs. (Bloomberg/SCMP/TechCrunch) CXMT’s Shanghai listing became the year’s biggest debut, with shares surging as much as 472% to value the DRAM maker nea

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industrial buyers. (TIME) Beyond Kimi K3, several other labs had model news worth flagging this week. Alibaba previewed a new flagship Qwen model that it says ranks second only to Anthropic’s Claude. (SCMP/Bloomberg); Qwen also shipped Qwen-Image-3.0, the third generation of its image-generation model, built around a single theme the company calls “Real.” (@Alibaba Qwen); Alibaba also open-sourced a new AI software stack aimed at loosening Nvidia’s grip on the CUDA ecosystem. (SCMP); Ant Group r

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uld be a composite one. Comparing model quality only means something if you compare at the same cost, because when you compare two cars you compare cars in the same price bracket; Anthropic is now ahead of OpenAI—is that durable? I don’t think it’s durable, it’s certainly a phase. OpenAI and Google will most likely keep trading places going forward; In the global division of labor in AI, the role Chinese companies are quite likely to play is still the largest producer. By ordinary logic, our pro

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Index (only beaten by Claude Fable and GPT-5.6 Sol Max while being cheaper ), 1 overall in Frontend Code Arena , and more impressive results . Moonshot AI is going toe to toe with Anthropic and OpenAI with far, far fewer resources. It is clearly the strongest open model ever released. It should be clear looking at this model that if adversarial distillation from the closed frontier models in the U.S. contributed, it is at most to a relatively small degree. AI observers who followed the distillat

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h markets. Moonshot AI released Kimi K3, which it's calling the world's largest open-weight model, and framed it as closing the gap with—and in some benchmarks rivaling—OpenAI and Anthropic. The release landed hard: Nvidia and other US Big Tech names sold off on renewed competitive fears, and the shockwaves compounded a brutal week for chip stocks. (Bloomberg/CNBC) CXMT prices Asia’s biggest listing of the year. Beyond Kimi K3 (above), the agent race kept accelerating this week. Alibaba and Hono

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e millions of them, some very big and some very small, and the value is in what you build on top - after all, every SaaS company is a ’database wrapper’. There’s a future in which Anthropic (or a company we haven’t heard of yet) wins the whole thing and can set its own terms, and a future in which dozens of routers run real-time auctions to allocate your tasks across hundreds of low-margin model-farms and a benchmark company takes a fee on every single one. I don’t think anyone can actually know

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AWS, Microsoft Azure, and Google Cloud – then it needs to leverage that advantage to make money. Being a neocloud does this. There is precedent for this. Back in November 2024, in Anthropic And OpenAI Show Why Musk Should Build A Cloud, I explained why Elon Musk needed to take better advantage of the enormous amount of money that was spent to build clusters to train its Grok AI models. And early this year, lo and behold, he turned the Colossus datacenters in Memphis into a neocloud and sold capa

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is now at 26 GW of contracted compute: 10 GW of NVIDIA systems and 16 GW of non-NVIDIA, the latter split between a 6 GW AMD MI450 commitment and 10 GW of Broadcom custom silicon. Anthropic is at 6 GW total, split between 1 GW of NVIDIA and 5 GW of non-NVIDIA capacity. On disclosed gigawatts, then, OpenAI’s non-NVIDIA book is now larger than its NVIDIA book, and larger than Anthropic’s entire portfolio. It is worth being careful about what that does and does not say. NVIDIA is still OpenAI’s lar

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17周人类工程工作的软件包（花费251美元的token）。同样，AI系统并非能通过所有测试，运行成本也不总是低廉，但它们确实在以非常快的速度进步。在我自己的实验中，我发现Fable能够自主工作9小时，执行非常复杂的软件项目，而这些项目原本需要一个团队花一周多的时间才能完成。 到目前为止，我关注的是前沿模型，即那些“智能”最高的模型。它们由三家美国公司制造——Anthropic、OpenAI和Google（不过Google已经有一段时间没有发布新模型了）。但还有第二类接近前沿的AI模型，通常比前沿落后6至12个月，它们全部来自中国。这些是开放权重模型，意味着任何人在发布后都可以使用或修改它们（与专有的前沿模型相对）。这使它们的运行成本相当低廉。它们同样在沿着指数级改进曲线攀升，尽管落后于美国的闭源模型。你可以从我制作的一张图表中看到这一点，该图展示的是AI在一项名为AA-Briefcase的测试中的表现，该测试模拟一个复杂的、为期数周的咨询项目，AI必须进行多种分析。开放权重的中国模型（其他国家也生产开放权重模型，但没有一个接近前沿）处于自己的指数曲线上，落后于美国的闭源模型 但抽象的图

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m a landlord who can cut them off. And it still doesn’t look like we’re doing nearly enough to change this before it is too late… — A week ago the United States government ordered Anthropic, the world’s most valuable AI start-up, to switch off its most capable model, Fable, for every foreign national on earth - whether they worked for Anthropic or not. This was not an export ban on a weapon sold to an adversary. It was an instruction to disable a commercial product, four days after its release,

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十年前百度美研一群人就预判：不能把 AI 算力全押在英伟达身上。结果他们真的投了一家「晶圆级」芯片公司 —Cerebras。 2016 年，周楠从投行跳去百度美国人工智能研究院。那时候吴恩达带队，预算充足，GPU 随便买。Dario（Anthropic 创始人）和 Greg Diamond 都在那里。团队已经隐约摸到 scaling law 的雏形：模型越大、数据越多、算力越强，效果就越好。DeepSpeech2 那篇论文就是最早的信号之一。 周楠做的第一个项目就是 Cerebras。当时他满世界找「不是英伟达」的训练芯片，看了 Graphcore、Wave Computing，最后选了 Cerebras。因为这家公司最激进——要做 wafer-scale engine，把一整片晶圆做成一个巨大的 AI 计算引擎，让计算单元和内存靠得极近，大幅降低通信成本。 投的时候 Cerebras 连流片都没有，只有 signature。百度美研的研究员直接上手验证，在当时全球最大的语言模型上跑，信号还不错。投决会几乎零阻力，Robin、陆奇、李彦宏快速通过。估值当时已经 7 亿多刀，在 2017

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areer Cisco, Nvidia team up to build fatter, faster pluggable If you thought 1.6 Tbps was fast, just wait, Cisco's USFP targets 12.8 Tbps in a single optical pluggable TOP STORIES Anthropic asks users to stop being mean to Claude; '123456' password linked to Denmark data breach affecting 8.8M people; Microsoft and Anthropic play invoice tennis with startup's $17,600 Claude bill; Starlink's plan to avoid orbital near-misses: Ephemeris sharing; Shai-Hulud worm makes jump to AI infrastructure with

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Large, expensive models are back - introducing “Smart Friend” If you look at the most popular models over the last few months, you see a distinct shift from mid-sized models like Anthropic’s Sonnet-class models to large models like Anthropic’s Opus-class models for the sake of performance. And with Mythos coming, we can basically say “scaling is back” The quiet implication of this is that frontier intelligence will soon be too expensive (and perhaps slow) for most day-to-day tasks. At the same

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mix of AIs being surprisingly gullible and other AIs doing a lot of gaslighting, exaggerating, and implying they’ve done a great job in their outputs.3 I haven’t seen AIs—at least Anthropic’s AIs—lie directly, clearly, and in an obviously intentional way. But on very hard tasks, it’s quite common for their outputs to be extremely misleading, or for them to be incorrect about a key thing seemingly because they were misled by another AI’s outputs. I’ve also seen AIs make up nonsensical excuses for

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ls is complexity that belongs in the system, not in the UI. If you have to choose, the tool isn't doing its job. That was true when one model was clearly the best. We started with Anthropic's Sonnet. It was the best thing available for real coding work, and it wasn't close. You could build around it, tune your prompts for it, and feel confident you weren't missing anything. Then GPT-5 showed up and was good enough that people wanted a choice. We shipped the picker. Then Opus 4.5 pulled ahead of

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at least fend off, co-opt and absorb the thousands of other people who are trying to do that. Third, while much of this applies to everyone else in the field as well, OpenAI, like Anthropic, has to ‘cross the chasm’ across the ‘messy middle’ (insert your favourite startup book title here) without existing products that can act as distribution and make all of this a feature, and to compete in one of the most capital-intensive industries in history without cashflows from existing businesses to lea

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